We use two pre-trained models. The first one, called `wav2vec-EN' is provided by Schneider et al. in  [1]}, trained on Librispeech corpus [2]} consisting of 960 hours of English audio book.
We also train our own model, called `wav2vec-FR' on French unlabeled call center conversations on over 500 hours of private-owned data.
Unfortunately, since no large conversational German database was at our disposal, we could not build a German model.
Features were extracted on an upsampledWe used FFMpeg resampling function with sinc interpolation function Allosat (from 8 to 16kHz) using both `wav2vec-FR' and `wav2vec-EN' and on the SEWA dataset using `wav2vec-EN'.
In the end, each emotional segment is represented by a 512-dimensional vector which consists of the averaged values of obtained embeddings over the emotional segment.
